Frontiers in Marine Science (Jun 2023)

Nonlocality of scale-dependent eddy mixing at the Kuroshio Extension

  • Mingyue Liu,
  • Ru Chen,
  • Wenting Guan,
  • Hong Zhang,
  • Tian Jing

DOI
https://doi.org/10.3389/fmars.2023.1137216
Journal volume & issue
Vol. 10

Abstract

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Although eddy parameterization schemes are often based on the local assumption, previous studies indicate that the nonlocality of total eddy mixing is prevalent at the Kuroshio Extension (KE). For eddy-permitting climate models, only mixing induced by eddies smaller than the resolvable scale of climate models (L*) needs to be parameterized. Therefore, here we aim to estimate and predict the nonlocality of scale-dependent eddy mixing at the KE region. We consider the separation scale L* ranging from 0.2∘ to 2.5∘, which is comparable to the typical resolution of the ocean component of climate models. Using a submesoscale-permitting model solution (MITgcm llc4320) and Lagrangian particles, we estimate the scale-dependent mixing (SDM) nonlocality ellipses and then diagnose the square root of the ellipse area (Ln,particle). Ln,particle is a metric to quantify the degree of SDM nonlocality. We found that, for all the available L* values we consider, the SDM nonlocality is prevalent in the KE region, and mostly elevated values of Ln,particle occur within the KE jet. As L* decreases from 2.5∘ to 0.2∘, the ratio Ln,particle/L* increases from 0.8 to 8.9. This result indicates that the SDM nonlocality is more non-negligible for smaller L*, which corresponds to climate models with relatively high resolution. As to the SDM nonlocality prediction, we found that compared to the conventional scaling and the curve-fitting methods, the random forest approach can better represent Ln,particle, especially in the coastal regions and within the intense KE jet. The area of the Eulerian momentum ellipses well capture the spatial pattern, but not the magnitude, of Ln,particle. Our efforts suggest that eddy parameterization schemes for eddy-permitting models may be improved by taking into account mixing nonlocality.

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